Cross component intra prediction mode

ABSTRACT

A method, computer program, and computer system is provided for encoding video data. Data corresponding to a video frame is received. One or more luma and chroma samples from are identified from the received image data. Two or more linear models are determined from the identified luma and chroma samples. The luma and chroma samples are classified into a number of categories corresponding to a number of linear models, and each category has one or more corresponding linear model parameters signaled to the bitstream.

FIELD

This disclosure relates generally to field of data processing, and moreparticularly to video encoding and decoding.

BACKGROUND

AOMedia Video 1 (AV1) is an open video coding format designed for videotransmissions over the Internet. It was developed as a successor to VP9by the Alliance for Open Media (AOMedia), a consortium founded in 2015that includes semiconductor firms, video on demand providers, videocontent producers, software development companies and web browservendors. AV1 specifies a chroma-from-luma mode that allows forchroma-only intra prediction and may model chroma pixels as a linearfunction of coincident reconstructed luma pixels.

SUMMARY

Embodiments relate to a method, system, and computer readable medium forencoding video data. According to one aspect, a method for encodingvideo data is provided. The method may include receiving datacorresponding to a video frame. One or more luma and chroma samples fromare identified from the received image data. Two or more linear modelsare determined from the identified luma and chroma samples. The luma andchroma samples are classified into a number of categories correspondingto a number of linear models, and each category has one or morecorresponding linear model parameters signaled to the bitstream.

According to another aspect, a computer system for encoding video datais provided. The computer system may include one or more processors, oneor more computer-readable memories, one or more computer-readabletangible storage devices, and program instructions stored on at leastone of the one or more storage devices for execution by at least one ofthe one or more processors via at least one of the one or more memories,whereby the computer system is capable of performing a method. Themethod may include receiving data corresponding to a video frame. One ormore luma and chroma samples from are identified from the received imagedata. Two or more linear models are determined from the identified lumaand chroma samples. The luma and chroma samples are classified into anumber of categories corresponding to a number of linear models, andeach category has one or more corresponding linear model parameterssignaled to the bitstream.

According to yet another aspect, a computer readable medium for encodingvideo data is provided. The computer readable medium may include one ormore computer-readable storage devices and program instructions storedon at least one of the one or more tangible storage devices, the programinstructions executable by a processor. The program instructions areexecutable by a processor for performing a method that may accordinglyinclude receiving data corresponding to a video frame. One or more lumaand chroma samples from are identified from the received image data. Twoor more linear models are determined from the identified luma and chromasamples. The luma and chroma samples are classified into a number ofcategories corresponding to a number of linear models, and each categoryhas one or more corresponding linear model parameters signaled to thebitstream.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages will become apparentfrom the following detailed description of illustrative embodiments,which is to be read in connection with the accompanying drawings. Thevarious features of the drawings are not to scale as the illustrationsare for clarity in facilitating the understanding of one skilled in theart in conjunction with the detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 is a function block diagram of a chroma-from-luma predictionprocess, according to at least one embodiment;

FIG. 3 is an operational flowchart illustrating the steps carried out bya program that encodes video data based on multiple linear models,according to at least one embodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, according to at leastone embodiment; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5, according to at least one embodiment.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. Those structures and methods may, however, beembodied in many different forms and should not be construed as limitedto the exemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope to those skilled in the art. Inthe description, details of well-known features and techniques may beomitted to avoid unnecessarily obscuring the presented embodiments.

Embodiments relate generally to the field of data processing, and moreparticularly to video encoding and decoding. The following describedexemplary embodiments provide a system, method and computer program to,among other things, encode videos using multiple linear models forchroma-from-luma prediction. Therefore, some embodiments have thecapacity to improve the field of computing by allowing for improvedchroma-from-luma prediction by considering luma samples within an imageboundary and calculating average luma values based on multiplecategories of parameters.

As previously described, AOMedia Video 1 (AV1) is an open video codingformat designed for video transmissions over the Internet. It wasdeveloped as a successor to VP9 by the Alliance for Open Media(AOMedia), a consortium founded in 2015 that includes semiconductorfirms, video on demand providers, video content producers, softwaredevelopment companies and web browser vendors. AV1 specifies achroma-from-luma mode that allows for chroma-only intra prediction andmay model chroma pixels as a linear function of coincident reconstructedluma pixels. However, in chroma-from-luma mode, only one linear modelmay be employed between luma and chroma samples within one whole codedblock, but the relationship between luma and chroma samples within onewhole coded block may not always be well fitted by one single linearmodel. Additionally, when some samples in co-located luma blocks are outof a picture boundary, these samples may be padded and used to calculatethe average of luma samples, which may cause increased complexity.Moreover, all the samples in the corresponding luma blocks may be usedto calculate the average luma values when chroma-from-luma mode may beselected, which may be complex when current block may be equal to orgreater than 32 pixels by 32 pixels. It may be advantageous, therefore,to determine one or more linear models for the luma and chroma samplesin order to improve cross component intra prediction and decreasecomplexity costs.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerreadable media according to the various embodiments. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer readable programinstructions.

Referring now to FIG. 1, a functional block diagram of a networkedcomputer environment illustrating a video frame encoding system 100(hereinafter “system”) for encoding video data based on multiple linearmodels. It should be appreciated that FIG. 1 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

The system 100 may include a computer 102 and a server computer 114. Thecomputer 102 may communicate with the server computer 114 via acommunication network 110 (hereinafter “network”). The computer 102 mayinclude a processor 104 and a software program 108 that is stored on adata storage device 106 and is enabled to interface with a user andcommunicate with the server computer 114. As will be discussed belowwith reference to FIG. 4 the computer 102 may include internalcomponents 800A and external components 900A, respectively, and theserver computer 114 may include internal components 800B and externalcomponents 900B, respectively. The computer 102 may be, for example, amobile device, a telephone, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing devices capable of running a program, accessing a network, andaccessing a database.

The server computer 114 may also operate in a cloud computing servicemodel, such as Software as a Service (SaaS), Platform as a Service(PaaS), or Infrastructure as a Service (laaS), as discussed below withrespect to FIGS. 6 and 7. The server computer 114 may also be located ina cloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud.

The server computer 114, which may be used for encoding video data isenabled to run a Video Encoding Program 116 (hereinafter “program”) thatmay interact with a database 112. The Video Encoding Program method isexplained in more detail below with respect to FIG. 3. In oneembodiment, the computer 102 may operate as an input device including auser interface while the program 116 may run primarily on servercomputer 114. In an alternative embodiment, the program 116 may runprimarily on one or more computers 102 while the server computer 114 maybe used for processing and storage of data used by the program 116. Itshould be noted that the program 116 may be a standalone program or maybe integrated into a larger video encoding program.

It should be noted, however, that processing for the program 116 may, insome instances be shared amongst the computers 102 and the servercomputers 114 in any ratio. In another embodiment, the program 116 mayoperate on more than one computer, server computer, or some combinationof computers and server computers, for example, a plurality of computers102 communicating across the network 110 with a single server computer114. In another embodiment, for example, the program 116 may operate ona plurality of server computers 114 communicating across the network 110with a plurality of client computers. Alternatively, the program mayoperate on a network server communicating across the network with aserver and a plurality of client computers.

The network 110 may include wired connections, wireless connections,fiber optic connections, or some combination thereof. In general, thenetwork 110 can be any combination of connections and protocols thatwill support communications between the computer 102 and the servercomputer 114. The network 110 may include various types of networks,such as, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, a telecommunication network such as thePublic Switched Telephone Network (PSTN), a wireless network, a publicswitched network, a satellite network, a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a metropolitan area network(MAN), a private network, an ad hoc network, an intranet, a fiberoptic-based network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 1 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 1. Furthermore, two or more devices shown in FIG. 1 may beimplemented within a single device, or a single device shown in FIG. 1may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) of system100 may perform one or more functions described as being performed byanother set of devices of system 100.

Referring now to FIG. 2, a function block diagram 200 for achroma-from-luma prediction process is depicted. Chroma-from-lumaprediction may be expressed as CfL(α)=α×L_(AC)+DC, where L_(AC) maydenote an AC contribution of luma component, α may denote a parameter ofthe linear model, and DC denotes a DC contribution of the chromacomponent. Reconstructed luma pixels may be subsampled by thesubsampling module 202 into the chroma resolution, and the average valuedetermined by the averaging module 204 may be subtracted to form the ACcontribution by the subtraction module 206. To approximate the chroma ACcomponent from the AC contribution, the parameter a is determined basedon the original chroma pixels and signals them in the bitstream. Theparameter a may be multiplied by the AC contribution by themultiplication module 208. The DC contribution of the chroma componentmay be computed using intra DC mode and may be added to the output ofthe multiplication module 208 by the addition module 210.

Multiple linear models (MLM) can be employed on top of chroma-from-lumabetween luma and chroma samples within one whole coded blocks, whereby anumber of linear models may be denoted as N, where N>1. When MLM modemay be selected, the samples within the luma and chroma block may beclassified into N categories, and each category may have its own linearmodel parameters. For example, the parameter a of N models may besignaled to the bitstream for each of the models. The samples in chromablock may be classified into N models based on their coordinates in thecoded block. According to one or more embodiments, the samples in theeven rows or columns may be classified into a first category and thesamples in odd rows or columns may be classified into category 1. In oneor more embodiments, the samples may be classified into two categoriesby one straight and one cross current block.

In one or more embodiments, the samples in chroma block may beclassified into N models based on the values of the correspondingreconstructed luma samples. In one or more embodiments, one threshold T1may be calculated based on the corresponding luma samples, whereby T1may be the average or median values of the samples within correspondingluma block. Each corresponding luma samples may be compared to thethreshold T1 to determine its category. For example, when the values ofcurrent luma samples may be smaller than T1, its corresponding chromasamples may be classified into the first category. Otherwise, thissample may be classified into the second category.

In one or more embodiments, the values of corresponding luma samples maybe compared to a DC value to determine its category. The DC value may bemedian value of the pixel value range, for example, the DC value may be128 when the internal bit-depth of the codec may be 8, and the DC valuemay be 512 when the internal bit-depth of the codec may be 10. In one ormore embodiments, the parameters of the first linear model may bedirectly signaled, and the difference of parameters α with the firstmodel may be signaled for the remaining linear models. In one or moreembodiments, for the remaining linear models, the difference of theparameters α with the first model may be restricted to [−K, K], K may bea positive integer, such as 1 or 2.

In one or more embodiments, the average luma values for each linearmodel may be computed by using the samples belonging to each individualcategory. In one or more embodiments, multiple linear models (MLM) andchroma-from-luma may be signaled together in one flag, namely crosscomponent mode (CCM). When the current mode may be CCM, then oneadditional flag may be signaled to indicate whether current mode may beMLM or chroma-from-luma. In one or more embodiments, MLM andchroma-from-luma may be signaled together with nominal angles andnon-directional modes.

When some of the corresponding luma samples may be out of the picture,these out-of-picture luma samples may be not padded or used forcalculating the average luma values for chroma-from-luma mode. In one ormore embodiments, all and only the samples within the picture may beused for calculating the average luma values for chroma-from-luma mode.In one or more embodiments, only the first M rows (and/or N columns)samples within the picture may be used for calculating the average lumavalues for chroma-from-luma mode, where M or N may be less than or equalto the height or width of a block, and the value of M and N may be bothpowers of 2.

Average luma values may be computed by using the samples in one ormultiple predefined positions when chroma-from-luma mode may be used. Inone or more embodiments, after down-sampling process, the luma samplesin the even/odd rows (or columns) may be utilized to calculate theaverage value. In one or more embodiments, after down-sampling process,the luma samples in corner positions and/or center positions may beutilized to calculate the average value.

One or multiple predicted linear model parameters (e.g., slope valuesand offset values) may be derived using neighboring reconstructed lumaand chroma samples, then one or multiple delta values between theactually used linear model parameters may be signaled. In one or moreembodiments, the delta values may be signaled using unary code. In oneor more embodiments, the delta values may be restricted to a predefinedrange, such as [−L,L], such that the computed delta values may be out ofthis range, it may be clipped to −L or L.

Referring now to FIG. 3, an operational flowchart 300 illustrating thesteps carried out by a program that encodes video data is depicted. FIG.3 may be described with the aid of FIGS. 1 and 2. As previouslydescribed, the Video Encoding Program 116 (FIG. 1) may quickly andeffectively encode videos using chroma-from-luma prediction based onmultiple linear models.

At 302, data corresponding to a video frame is received. The data may bea still image or may video data from which one or more frames may beextracted. In operation, the Video Encoding Program 116 (FIG. 1) on theserver computer 114 (FIG. 1) may receive video frame data from thecomputer 102 (FIG. 1) over the communication network 110 (FIG. 1) or mayretrieve the video frame data from the database 112 (FIG. 1).

At 304, one or more luma and chroma samples are identified from thereceived image data. The luma and chroma samples may allow for intraframe prediction to allow for compression of the video frame data. Inoperation, the Video Encoding Program 116 (FIG. 1) may extract the lumaand chroma samples from the received video frame data. The extractedluma and chroma samples may be stored within the database 112 (FIG. 1).

At 306, two or more linear models are determined from the identifiedluma and chroma samples. The luma and chroma samples are classified intoa number of categories corresponding to a number of linear models, andeach category has one or more corresponding linear model parameterssignaled to the bitstream. In operation, the Video Encoding Program 116(FIG. 1) may determine one or more linear models based on the luma andchroma samples stored in the database 112 (FIG. 1) or thechroma-from-luma prediction output of the multiplication module 208(FIG. 2).

It may be appreciated that FIG. 3 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

FIG. 4 is a block diagram 400 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment. It should be appreciated that FIG. 4 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

Computer 102 (FIG. 1) and server computer 114 (FIG. 1) may includerespective sets of internal components 800A,B and external components900A,B illustrated in FIG. 4. Each of the sets of internal components800 include one or more processors 820, one or more computer-readableRAMs 822 and one or more computer-readable ROMs 824 on one or more buses826, one or more operating systems 828, and one or morecomputer-readable tangible storage devices 830.

Processor 820 is implemented in hardware, firmware, or a combination ofhardware and software. Processor 820 is a central processing unit (CPU),a graphics processing unit (GPU), an accelerated processing unit (APU),a microprocessor, a microcontroller, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or another type of processing component. In someimplementations, processor 820 includes one or more processors capableof being programmed to perform a function. Bus 826 includes a componentthat permits communication among the internal components 800A,B.

The one or more operating systems 828, the software program 108 (FIG. 1)and the Video Encoding Program 116 (FIG. 1) on server computer 114(FIG. 1) are stored on one or more of the respective computer-readabletangible storage devices 830 for execution by one or more of therespective processors 820 via one or more of the respective RAMs 822(which typically include cache memory). In the embodiment illustrated inFIG. 4, each of the computer-readable tangible storage devices 830 is amagnetic disk storage device of an internal hard drive. Alternatively,each of the computer-readable tangible storage devices 830 is asemiconductor storage device such as ROM 824, EPROM, flash memory, anoptical disk, a magneto-optic disk, a solid state disk, a compact disc(CD), a digital versatile disc (DVD), a floppy disk, a cartridge, amagnetic tape, and/or another type of non-transitory computer-readabletangible storage device that can store a computer program and digitalinformation.

Each set of internal components 800A,B also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1) and the Video Encoding Program 116 (FIG. 1) can bestored on one or more of the respective portable computer-readabletangible storage devices 936, read via the respective R/W drive orinterface 832 and loaded into the respective hard drive 830.

Each set of internal components 800A,B also includes network adapters orinterfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interfacecards; or 3G, 4G, or 5G wireless interface cards or other wired orwireless communication links. The software program 108 (FIG. 1) and theVideo Encoding Program 116 (FIG. 1) on the server computer 114 (FIG. 1)can be downloaded to the computer 102 (FIG. 1) and server computer 114from an external computer via a network (for example, the Internet, alocal area network or other, wide area network) and respective networkadapters or interfaces 836. From the network adapters or interfaces 836,the software program 108 and the Video Encoding Program 116 on theserver computer 114 are loaded into the respective hard drive 830. Thenetwork may comprise copper wires, optical fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers.

Each of the sets of external components 900A,B can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900A,B can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800A,B also includes device drivers 840to interface to computer display monitor 920, keyboard 930 and computermouse 934. The device drivers 840, R/W drive or interface 832 andnetwork adapter or interface 836 comprise hardware and software (storedin storage device 830 and/or ROM 824).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,some embodiments are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (laaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring to FIG. 5, illustrative cloud computing environment 500 isdepicted. As shown, cloud computing environment 500 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 600 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 500 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring to FIG. 6, a set of functional abstraction layers 600 providedby cloud computing environment 500 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments are notlimited thereto. As depicted, the following layers and correspondingfunctions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and Video Encoding 96. Video Encoding 96 mayencode video data based on multiple linear models for chroma-from-lumaprediction.

Some embodiments may relate to a system, a method, and/or a computerreadable medium at any possible technical detail level of integration.The computer readable medium may include a computer-readablenon-transitory storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outoperations.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program code/instructions for carrying out operationsmay be assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, configuration datafor integrated circuitry, or either source code or object code writtenin any combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects or operations.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer readable media according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). The method, computer system, and computerreadable medium may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in theFigures. In some alternative implementations, the functions noted in theblocks may occur out of the order noted in the Figures. For example, twoblocks shown in succession may, in fact, be executed concurrently orsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

The descriptions of the various aspects and embodiments have beenpresented for purposes of illustration, but are not intended to beexhaustive or limited to the embodiments disclosed. Even thoughcombinations of features are recited in the claims and/or disclosed inthe specification, these combinations are not intended to limit thedisclosure of possible implementations. In fact, many of these featuresmay be combined in ways not specifically recited in the claims and/ordisclosed in the specification. Although each dependent claim listedbelow may directly depend on only one claim, the disclosure of possibleimplementations includes each dependent claim in combination with everyother claim in the claim set. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope of the described embodiments. The terminology used herein waschosen to best explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method of video encoding, executable by aprocessor, the method comprising: receiving image data corresponding toa video frame; identifying one or more luma and chroma samples from thereceived image data, wherein the chroma samples are predicted from theluma samples based on a DC contribution from a chroma component of thereceived image data and an AC contribution from a luma component of thereceived image data; and determining two or more linear models from theidentified luma and chroma samples, wherein the luma and chroma samplesare classified into a number of categories corresponding to a number oflinear models, and wherein each category has one or more correspondinglinear model parameters signaled to the bitstream.
 2. The method ofclaim 1, wherein the chroma samples are classified into one or moremodels based on coordinates associated with the chroma samples.
 3. Themethod of claim 2, wherein chroma samples corresponding to even rows orcolumns are classified into a first category and chroma samplescorresponding to odd rows or columns are classified into a secondcategory.
 4. The method of claim 1, wherein the chroma samples areclassified into the linear models based on one or more values ofcorresponding reconstructed luma samples.
 5. The method of claim 1,wherein the parameters of a first linear model from among the linearmodels are directly signaled, and a difference between one or more ofthe parameters and the first linear model are signaled for the remaininglinear models.
 6. The method of claim 1, wherein one or more averageluma values for each linear model are computed by using the samplesbelonging to each of the categories.
 7. The method of claim 1, wherein afirst flag signals the linear models and a status corresponding to achroma-from-luma mode are in a cross-component mode and a second flagsignals whether a current mode corresponds to the linear models or achroma-from-luma mode.
 8. The method of claim 1, wherein one or moreluma samples outside of boundaries associated with the image data arenot used for calculating the average luma values for chroma-from-lumamode.
 9. The method of claim 1, wherein one or more average luma valuesare computed by using the one or more luma samples in one or multiplepredefined positions when a chroma-from-luma mode is used.
 10. Themethod of claim 1, wherein one or predicted linear model parameters arederived using neighboring reconstructed luma and chroma samples.
 11. Acomputer system for encoding video data, the computer system comprising:one or more computer-readable non-transitory storage media configured tostore computer program code; and one or more computer processorsconfigured to access said computer program code and operate asinstructed by said computer program code, said computer program codeincluding: receiving code configured to cause the one or more computerprocessors to receive image data corresponding to a video frame;identifying code configured to cause the one or more computer processorsto identify one or more luma and chroma samples from the received imagedata, wherein the chroma samples are predicted from the luma samplesbased on a DC contribution from a chroma component of the received imagedata and an AC contribution from a luma component of the received imagedata; and determining code configured to cause the one or more computerprocessors to determine two or more linear models from the identifiedluma and chroma samples, wherein the luma and chroma samples areclassified into a number of categories corresponding to a number oflinear models, and wherein each category has one or more correspondinglinear model parameters signaled to the bitstream.
 12. The computersystem of claim 11, wherein the chroma samples are classified into oneor more models based on coordinates associated with the chroma samples.13. The computer system of claim 12, wherein chroma samplescorresponding to even rows or columns are classified into a firstcategory and chroma samples corresponding to odd rows or columns areclassified into a second category.
 14. The computer system of claim 11,wherein the chroma samples are classified into the linear models basedon one or more values of corresponding reconstructed luma samples. 15.The computer system of claim 11, wherein the parameters of a firstlinear model from among the linear models are directly signaled, and adifference between one or more of the parameters and the first linearmodel are signaled for the remaining linear models.
 16. The computersystem of claim 11, wherein one or more average luma values for eachlinear model are computed by using the samples belonging to each of thecategories.
 17. The computer system of claim 11, wherein a first flagsignals the linear models and a status corresponding to achroma-from-luma mode are in a cross-component mode and a second flagsignals whether a current mode corresponds to the linear models or achroma-from-luma mode.
 18. The computer system of claim 11, wherein oneor more luma samples outside of boundaries associated with the imagedata are not used for calculating the average luma values forchroma-from-luma mode.
 19. The computer system of claim 11, wherein oneor more average luma values are computed by using the one or more lumasamples in one or multiple predefined positions when a chroma-from-lumamode is used.
 20. A non-transitory computer readable medium havingstored thereon a computer program for encoding video data, the computerprogram configured to cause one or more computer processors to: receiveimage data corresponding to a video frame; identify one or more luma andchroma samples from the received image data, wherein the chroma samplesare predicted from the luma samples based on a DC contribution from achroma component of the received image data and an AC contribution froma luma component of the received image data; and determine two or morelinear models from the identified luma and chroma samples, wherein theluma and chroma samples are classified into a number of categoriescorresponding to a number of linear models, and wherein each categoryhas one or more corresponding linear model parameters signaled to thebitstream.